Assessing the impact of AR-HUD warning systems on cyclists’ crash risk in connected environments: incorporating crash probability and severity

{"In":[0,115],"connected":[1,80,250],"environments,":[2],"Head-Up":[3,9],"Display":[4,10],"(HUD)":[5],"and":[6,51,71,95,125,142,169,180,186,207,240,245,261,280],"Augmented":[7],"Reality":[8],"(AR-HUD)":[11],"alert":[12],"drivers":[13],"within":[14],"their":[15,20,268],"line":[16],"of":[17,39,49,243],"sight,":[18],"yet":[19],"effects":[21],"on":[22,43,91,265],"crash":[23,47,54,112,119,126,175,178,213,225,282],"risk":[24,48,55,157,214,226],"in":[25,78,249],"cyclist":[26,67],"occupying":[27,68],"lane":[28],"scenarios":[29],"remain":[30],"unclear.":[31],"This":[32],"study":[33],"aims":[34],"to":[35,105,135,223,270],"quantify":[36],"the":[37,44,66,92,102,149,159,165,229,237,253],"impact":[38],"different":[40,58,212],"warning":[41,73,161,217,247],"systems":[42,74,248],"\\"probability-severity\\"":[45],"integrated":[46,130],"cyclists":[50],"explore":[52],"potential":[53],"characteristics":[56,215],"across":[57,216,228],"driver":[59,201],"groups.":[60],"Using":[61],"a":[62,79,117,234],"driving":[63,208,266],"simulation":[64],"platform,":[65],"road":[69],"scenario":[70],"three":[72,160],"(Baseline/HUD/AR-HUD)":[75],"were":[76,88,171,255],"constructed":[77],"environment.":[81],"Driving":[82],"behavior":[83],"data":[84],"from":[85,257],"38":[86],"participants":[87],"collected.":[89],"Based":[90],"research":[93],"objective":[94],"conflict":[96,156],"mechanism,":[97],"DRAC":[98],"was":[99,123,128],"selected":[100],"as":[101],"primary":[103],"indicator":[104],"develop":[106],"an":[107],"Extreme":[108],"Value":[109],"Theory":[110],"(EVT)-based":[111],"probability":[113,121,127],"model.":[114],"addition,":[116],"Delta-V-based":[118],"severity":[120,134],"model":[122,152],"established,":[124],"further":[129,198,241,275],"with":[131,164,173,189],"conditional":[132],"injury":[133],"derive":[136],"standardized":[137,181],"expected-risk":[138,182],"estimates":[139,183],"for":[140,184,236],"severe":[141,177,185],"non-severe":[143,187],"crashes.":[144],"The":[145],"results":[146],"showed":[147],"that":[148,200],"DRAC-based":[150],"EVT":[151],"reasonably":[153],"characterized":[154],"extreme":[155],"under":[158,193],"conditions.":[162],"Compared":[163],"Baseline,":[166],"both":[167],"HUD":[168,244],"AR-HUD":[170,220,246],"associated":[172],"lower":[174,224],"probabilities,":[176,179],"crashes,":[188],"larger":[190],"reductions":[191],"observed":[192,281],"AR-HUD.":[194],"Exploratory":[195],"subgroup":[196],"analyses":[197],"indicated":[199],"groups":[202],"defined":[203],"by":[204],"gender,":[205],"age,":[206],"experience":[209],"may":[210],"exhibit":[211],"conditions,":[218],"while":[219],"generally":[221],"corresponded":[222],"levels":[227],"subgroups.":[230],"These":[231],"findings":[232,254],"provide":[233],"reference":[235],"safety":[238,259],"evaluation":[239],"optimization":[242],"environments.":[251],"Because":[252],"derived":[256],"surrogate":[258],"indicators":[260],"statistical":[262],"models":[263],"based":[264],"simulation,":[267],"applicability":[269],"real-world":[271],"traffic":[272],"conditions":[273],"requires":[274],"validation":[276],"using":[277],"real-vehicle":[278],"experiments":[279],"data.":[283]}

Authors

Institutions

Publication Details

Journal
Accident Analysis & Prevention
Published
2026-09-17
DOI
https://doi.org/10.1016/j.aap.2026.108777
Primary Topic
Traffic and Road Safety
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Assessing the impact of AR-HUD warning systems on cyclists’ crash risk in connected environments: incorporating crash probability and severity

Xiaohua Zhao, Yu Zhang, Xingxin Yi, Jianling Huang et al.
Accident Analysis & Prevention
Traffic and Road Safety
article

Assessing the impact of AR-HUD warning systems on cyclists’ crash risk in connected environments: incorporating crash probability and severity

Xiaohua Zhao, Yu Zhang, Xingxin Yi, Jianling Huang, Yang Bian
article en

Abstract

In connected environments, Head-Up Display (HUD) and Augmented Reality Head-Up Display (AR-HUD) alert drivers within their line of sight, yet their effects on crash risk in cyclist occupying lane scenarios remain unclear. This study aims to quantify the impact of different warning systems on the "probability-severity" integrated crash risk of cyclists and explore potential crash risk characteristics across different driver groups. Using a driving simulation platform, the cyclist occupying road scenario and three warning systems (Baseline/HUD/AR-HUD) were constructed in a connected environment. Driving behavior data from 38 participants were collected. Based on the research objective and conflict mechanism, DRAC was selected as the primary indicator to develop an Extreme Value Theory (EVT)-based crash probability model. In addition, a Delta-V-based crash severity probability model was established, and crash probability was further integrated with conditional injury severity to derive standardized expected-risk estimates for severe and non-severe crashes. The results showed that the DRAC-based EVT model reasonably characterized extreme conflict risk under the three warning conditions. Compared with the Baseline, both HUD and AR-HUD were associated with lower crash probabilities, severe crash probabilities, and standardized expected-risk estimates for severe and non-severe crashes, with larger reductions observed under AR-HUD. Exploratory subgroup analyses further indicated that driver groups defined by gender, age, and driving experience may exhibit different crash risk characteristics across warning conditions, while AR-HUD generally corresponded to lower crash risk levels across the subgroups. These findings provide a reference for the safety evaluation and further optimization of HUD and AR-HUD warning systems in connected environments. Because the findings were derived from surrogate safety indicators and statistical models based on driving simulation, their applicability to real-world traffic conditions requires further validation using real-vehicle experiments and observed crash data.

Accident Analysis & PreventionVol. 238
Beijing University of Technology (CN), Guangxi Transportation Research Institute (CN)
Climate action
Openalex Percentile: Top 11%
Traffic and Road Safety
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.